DR has become a popular method to promote generalization, particularly when training an agent in a (necessarily imperfect) simulator, and then deploying it on real hardware [1]}.
While the LiDAR data modality generally transfers better that vision (camera images) from simulation to the real world scenario
it is not without challenges since no sensor model is perfect. Just training the likelihood models without any variability in the simulator results in over-fitting and poor transfer. Since we do not have an exact recreation of the real world environment within our simulator, our learned agent will have to generalize to a new environment while simultaneously bridging the reality gap. Hence, it is important to account for various real world irregularities while training on the simulator. In our data collection pipeline, we randomized the following parameters:
